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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Mathur, Alok Pradhan, Shreyaan Soni, Prasoon Patel, Dhruvil Regunathan, Rajeshkannan Vellore Institute of Technology School of Computer Science Engineering Vellore India

ISBN: (纸本)9798350397376

Test case generation for a given topic can be a challenging and time-consuming task, particularly when the conversation about the topic includes boundary conditions and requirements. This process requires a complete understanding of the system and its scenarios to cover all possible examples, both good and bad. This paper proposes an automated solution for generating test cases using natural language processing techniques. The proposed methodology uses T5 and GPT-3 models to extract the context and topic of the conversation and generate test cases. The model identifies relevant keywords and generates test cases based on them, providing context-based meanings of specific words and defining new terms used in the conversation. The proposed approach eliminates the need for manual test case generation and allows for the testing of more complex systems with a larger number of scenarios. This will reduce the dependency on the expertise of the test designer, resulting in complete coverage of all scenarios. Additionally, this automation will save time and resources, making the process of test case generation more manageable. By utilizing NLP techniques, it will help to ensure that all scenarios are taken into account and that the test cases generated are comprehensive and accurate. © 2023 IEEE.

关键词： Natural language processing systems

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Efficient Object Detection on Sparse-to-Dense Depth Prediction 9

Efficient Object Detection on Sparse-to-Dense Depth Predicti...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Megala, G. Swarnalatha, P. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350397376

Detecting objects has been an important and intricate field for many years, particularly in the safety and healthcare sectors as well as the automotive industry. Much research on identifying objects has been shown to be a challenging one. When compared to other existing algorithms, You Only Look Once (YOLO) provides superior power and speed for this task. Semantic segmentation is a computer vision challenge in which the different parts of an image are sorted into classes that can be understood based on their meaning. When it is said that a class is semantically subject to interpretation, it means that it has some significance in the real world. The scope of the work is to detect the objects in an image and draw a bounding box around the object with its class and confidence score. The experimental results show that the objects are detected, classified, and localized with their confidence score. © 2023 IEEE.

关键词： Automotive industry

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Exploring the scope of Blockchain-based Central Bank Digital Currencies (CBDCs) in Crowdfunding Platforms 7

Exploring the scope of Blockchain-based Central Bank Digital...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Ghosh, Satyajit Jaiswal, Rishabh Das, Tapan Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798331527822

This paper introduces an innovative crowdfunding platform utilizing Central Bank Digital Currency (CBDC) to tackle the challenges linked to conventional payment methods and the regulatory limitations of earlier cryptocurrency-based solutions. We present a practical prototype, demonstrating the feasibility of the CBDC approach. An extensive evaluation comparing CBDCs with Ethereum and traditional systems confirms its superior performance. This research marks the initial step toward understanding the potential of CBDCs in crowdfunding, setting the stage for future exploration and innovation in this evolving financial landscape. © 2024 IEEE.

关键词： Blockchain CBDC Cross-border Payment Crowdfunding Digital Currency

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Optimizing IoT Security: A Machine Learning Pipeline for Fast and Accurate Intrusion Detection 7

Optimizing IoT Security: A Machine Learning Pipeline for Fas...

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Balaji, Keertan Chavan, Kunal Varma, Smridh Thikkireddy, Srikara Rao Department of Computer Science and Engineering S.R.M Institute of Technology Chennai India SENSE Vellore Institute of Technology Vellore India SCOPE Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331519513

After Industrial Revolution 4.0, there has been a drastic increase in IoT devices, proportionally affecting network traffic exponentially. This requires the advancement of a more efficient anomaly-detecting system, a technique to identify and counteract cyber threats. Although several systems have been published, they lack generalizability to real-time testing. This paper presents a network intrusion detection and prevention system that utilizes machine learning and ensemble learning algorithms that are trained to ensure a highly efficient and robust anomaly detection system. In this paper, we implement a three-stage prediction pipeline. The effectiveness of the proposed system is determined by calculating performance metrics such as accuracy, recall, precision, F1 score, and inference time and comparing them with those of other deep learning models. Furthermore, the proposed method is capable of classifying the various anomalies into 6 broad groups such as DDoS, DoS, Mirai, Web-based attacks, etc. The generalizability of this system is also proven by deploying it in a real-time IoT network environment for classification and evaluation. © 2024 IEEE.

关键词： Network intrusion

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Comparative Study of Disease Prediction Through Data Mining 2

Comparative Study of Disease Prediction Through Data Mining

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Karthikeyan, D. Aameer Arshath, A. Gunasekaran, V. Uthra, B. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The growing occurrence of persistent liver disorder has been cited in current times. Similarly Diabetes, one of the frequently spreading disease is growing hastily in all of the Nations nowadays. According to WHO, approximately 422 million humans globally have diabetes and 1.5 million deaths are without delay associated with diabetes disorder. So the effective analysis should be made to detect the presence of disease before it kills the patient which reflects a plenty of studies have been made in the field of healthcare. In this paper, we are going to predict these two diseases through one of the data mining techniques called classification. The analysis is completed using Jupyter Notebook. One of the data mining techniques called supervised learning or the classification is applied in which we have used Decision tree, Logistic Regression, Random Forest, K-Nearest Neighbor. The data in this research paper is taken from University of California Irvine repository of machine learning. The main aim of this paper is to provide brief prediction on both diabetes and liver disease and the goal is to provide a better final results when compared to the other algorithms, for the disease prediction. In results we are predicting the presence of diabetes disease with 98% of accuracy with Random forest algorithm and prediction of liver disorder has 81.59% of accuracy with Random Forest algorithm along with accuracy we have attempted to use some parameters like precision, recall, F-Measure for the evaluation. © 2024 IEEE.

关键词： Forecasting

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Seasonal Forest Disaster Prediction using Machine Learning Models 5

Seasonal Forest Disaster Prediction using Machine Learning M...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Niranjan, J. Krishnamoorthy, N. Vinothkumar, V. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379884

The forests are natural barriers that help prevent natural disasters such as heavy rainfall, heavy winds, etc. Because these disasters affect the forest, they make way for more disasters in surrounding areas including other forests, affecting animals, cities, and villages, especially tribal populations. This project aims to find solutions by analyzing the forest and its historical data including disaster incidents. The Seasonal Forest Disaster Analysis project harnesses advanced Machine Learning to investigate and mitigate the impact of forest disasters comprehensively. In essence, the Seasonal Forest Disaster Analysis project stands as a testament to the potential of cutting-edge technology and collaborative efforts in tackling some of the most pressing challenges facing our natural world. The Seasonal Forest Disaster Analysis project is a comprehensive endeavor that harnesses advanced Machine Learning & Deep Learning techniques to deeply investigate and mitigate the impact of forest disasters. Three models like CNN with Grad-CAM, ResNet and XceptionNet were proposed, out of which CNN with Grad-CAM produces an Accuracy of 94 %. By integrating a diverse array of datasets, including satellite imagery, climate data, and historical records, the project is geared towards effectively identifying, categorizing, and predicting a spectrum of seasonal forest disasters, including but not limited to wildfires, droughts, and floods. This integration enables the creation of a sophisticated framework that not only examines past occurrences but also foresees potential future hazards through predictive modelling. © 2024 IEEE.

关键词： Drought

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Detecting and Fixing SQL Injection and Cross-Site Scripting Vulnerabilities in Web Applications

Detecting and Fixing SQL Injection and Cross-Site Scripting ...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Sivaraman, Arjun Furniturewala, Mahera Gayathri, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350325188

More people have access to the internet than ever before. This has prompted many organizations to develop web-based applications that users can use online to interact with the organization. Poorly written code for web applications, especially SQL queries can be exploited to gain unauthorized access to sensitive data and web servers. It is not possible to manually scroll through the codebase to detect parts of code that would make the application vulnerable to attacks. Thus, machine learning and natural language processing-based solutions would help detect such queries either based on their patterns or signatures or through training via previous attack datasets. This paper aims to propose a novel methodology to detect SQL injection vulnerable queries and Cross-Site Scripting (XSS) attacks using pattern matching and NLP techniques and rewrite or in other words correct the vulnerable parts of code to prevent such attacks. Overall, this paper is used to identify vulnerable parts of code in a given client-side PHP web application, detect if the vulnerability belongs to a particular attack type and finally correct the vulnerable section of the code by providing recommendations based on standard prevention techniques for the corresponding attacks. © 2023 IEEE.

关键词： AdaBoost Cross-Site Scripting Machine Learning Naive Bayes Natural Language Processing Regular Expressions SQL Injection Tokenization

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An Efficient Vehicle Detection and Shadow Removal Using Gaussian Mixture Models with Blob Analysis for Machine Vision Application

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SN computer science 2023年第5期4卷 451页

作者： Rajkumar, S. Hariharan, A. Girish, S. Arulmurugan, Manoj School of Computer Science and Engineering Vellore Institute of Technology Chennai India

Cities in the modern era often face a hectic challenge due to increased urbanization and industrialization. This problem arises as the number of cars on the road increases over time, and the need for traffic data becomes important. This increase is not foreseen with the creation of suitable new road sections. Accordingly, in this paper, we developed a novel algorithm to detect and count vehicles of each frame from a CCTV footage after enhancing it accordingly. Morphological filtering is used for removal of noise from each frame. The vehicles are detected using blob analysis and detected as well as counted by Gaussian mixture models. Finally, we compare our proposed method’s results against a suitable and a recent method’s YOLO (you only look once algorithm) result. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bilateral filtering Blob analysis CCTV Foreground extraction Gaussian mixture models Morphological filtering

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A Novel Key Generation Algorithm Utilizing Lattice-Based Cryptography Principles 3rd

A Novel Key Generation Algorithm Utilizing Lattice-Based Cry...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Arul, Rajakumar Sivaselvam, B. Choudhary, Ankush Centre for Smart Grid Technologies Vellore Institute of Technology Chennai India Department of Computer Science and Engineering IIITDM Kancheepuram Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819726707

technology advancement is inevitable, and it has created a revolution in all the sectors creating huge opportunities. Artificial intelligence and machine learning have boosted the advancement of all the sectors making the life easier and comfortable. On the other hand, these advancements highly depend on the connectivity through which all the terminals are communicating. Thus, all the technology developments depend on the communication paradigm, particularly on the wireless medium. Securing the information and infrastructure is crucial, and it demands highly complex problems that cannot be breached with high computation machines like quantum computer. The emergence of quantum computer has made all the existing cryptographic algorithms vulnerable creating a space for advanced algorithms that are safe from quantum computer attacks. Key generation is the foundational process of any cryptographic algorithm, and in this research, we propose a novel key generation mechanism based on lattice using mathematical functions. The proposal is evaluated using theoretical assumptions, and it is found to be safe and secure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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A Digital Forensic Analysis of Profiling and Avoidance of Websites Disseminating Disinformation 2

A Digital Forensic Analysis of Profiling and Avoidance of We...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Agrawal, Harsh Singh, Shaurya Pratap Dixit, Shashwat Nagdev, Parth Priya, V. Thaseen, Sumaiya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The internet is an essential source of data for individuals across the world. However, disinformation has emerged as a significant problem due to its rapid growth on social media and the prevalence of fallacious news. Conventional strategies for detecting disinformation sites, like manual examination, are tedious and time-wasting. As a result, it is necessary to develop an effective method to find such websites. The goal of this research is to find websites that spread false information and prevent its further spread. The methodology is unique and effective for detecting disinformation websites as it makes use of the OSINT tool Maltego and focuses on websites that spread news having a negative impact such as in politics, health, finance, etc. Thus, the outcome of this research helps users to detect the authenticity of news in an easy and user-friendly manner by footprinting mechanisms. © 2024 IEEE.

关键词： Websites

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